Explainable AI for Precise Leaf Disease Diagnosis: A Comparative Study

S. Balanageshwara; Varuna Kumara; Manjunatha Badiger; Akshatha Naik

doi:10.48084/etasr.16335

Authors

S. Balanageshwara Department of Electronics and Communication Engineering, Moodlakatte Institute of Technology, Kundapura, Karnataka, India
Varuna Kumara Department of Electronics and Communication Engineering, Moodlakatte Institute of Technology, Kundapura, Karnataka, India
Manjunatha Badiger Department of VLSI Design and Technology, NMAM Institute of Technology, Nitte (Deemed to be University), Nitte, Karnataka, India
Akshatha Naik Department of Electronics and Communication Engineering, Moodlakatte Institute of Technology, Kundapura, Karnataka, India

Volume: 16 | Issue: 2 | Pages: 33806-33812 | April 2026 | https://doi.org/10.48084/etasr.16335

Received: 17 November 2025 | Revised: 30 November 2025, 5 December 2025, 14 December 2025, 4 January 2026, and 6 January 2026 | Accepted: 9 January 2026 | Online: 3 April 2026

Corresponding author: Manjunatha Badiger

Abstract

Sustainable agriculture depends on the timely identification of plant diseases. While deep learning, particularly Convolutional Neural Networks (CNNs), has shown promise in leaf disease classification, its black-box nature undermines transparency and farmer trust. This study presents a framework integrating EfficientNet-B4/B7 with multiple Explainable AI (XAI) techniques—Grad-CAM++, Score-CAM, Integrated Gradients, and LIME—to enhance interpretability. The proposed framework was developed and tested on the PlantVillage dataset. Although the classification model itself demonstrated modest performance (AUC 0.53), highlighting challenges in generalization, the primary contribution of this study lies in the comparative analysis of XAI methods. A qualitative expert evaluation revealed Grad-CAM++ as the most consistent and visually coherent method for highlighting disease-related features. A prototype farmer-friendly GUI was developed to visualize input images, predicted classes, and XAI heatmaps. This work underscores the importance of interpretability in building trustworthy AI systems for agriculture and provides a comparative baseline for XAI techniques in plant pathology, establishing a foundation for future work on more robust classifiers.

Keywords:

leaf disease detection, Explainable AI (XAI), EfficientNet, model interpretability, PlantVillage dataset

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